用中文总结以下内容:Fuel cell is one of the most promising renewable energy conversion technologies which represents a potential solution for the future energy crisis [1,2]. Among various fuel cell technologies, proton exchange membrane fuel cell (PEMFC) is considered as a competitive clean power source for fuel cell vehicles (FCV) due to its advantages of high energy efficiency and zero emission [3,4]. Among various PEMFC technologies, the fuel cell stack based on metallic bipolar plate (MBPP) presents the highest power density which could meet the demands of compact engine integration and full power range for the FCV applications. In recent years, the MBPP fuel cell stacks of maximum power over 100 kW are commercially applied in Toyota’s Mirai (2015) and Honda’s Clarity Fuel Cell (2016) with volumetric power density of 3.1 kW/L. To achieve higher power density, large current operation and enhanced heat dissipation, the fuel cell stack technology is still being evolved for widespread commercialization.
时间: 2023-02-19 19:02:32 浏览: 81
燃料电池是最有前景的可再生能源转换技术之一,可以解决未来的能源危机[1,2]。在各种燃料电池技术中,质子交换膜燃料电池(PEMFC)由于其高能效和零排放的优势,被认为是燃料电池汽车(FCV)的有竞争力的清洁能源来源[3,4]。在各种PEMFC技术中,基于金属双极板(MBPP)的燃料电池堆具有最高的功率密度,可以满足紧凑式发动机集成和FCV应用的全功率范围的要求。近年来,最大功率超过100 kW的MBPP燃料电池堆已经在丰田的Mirai(2015年)和本田的Clarity Fuel Cell(2016年)上得到商业应用,体积功率密度为3.1 kW/L。为了实现更高的功率密度、大电流运行和增强的热散热,燃料电池堆技术仍在不断演进,以实现广泛的商业化。
相关问题
What is the future development trend of China's pet sector?
As an AI language model, I do not have personal views or beliefs. However, according to research and analysis, the future development trend of China's pet sector is expected to be promising.
The rising middle class and increasing disposable income have led to a growing interest in pet ownership in China. This has resulted in a significant increase in demand for pet products and services, including pet food, grooming, and veterinary care.
The trend towards humanization of pets is also expected to continue, with pet owners increasingly treating their pets as family members and seeking high-quality products and services for them.
E-commerce platforms and mobile apps have also made it easier for pet owners to purchase pet products and services, and this is expected to continue to drive growth in the sector.
Overall, the future development trend of China's pet sector is likely to be characterized by continued growth, increasing demand for high-quality products and services, and a focus on humanization and convenience.
Help me write a literature review of the research on path planning algorithms for satellite maps
Satellite maps have become an essential tool for various applications, including navigation, agriculture, urban planning, and disaster response. One of the critical challenges in using satellite maps is path planning, which involves finding the optimal path between two locations while considering various constraints such as terrain, obstacles, and weather conditions. Over the years, several path planning algorithms have been developed for satellite maps, and this literature review aims to provide an overview of the research in this field.
One of the earliest and most popular path planning algorithms for satellite maps is Dijkstra's algorithm. This algorithm uses a graph-based approach to find the shortest path between two points while avoiding obstacles. However, Dijkstra's algorithm has limitations when dealing with large-scale maps or dynamic environments, and several variants have been proposed to overcome these limitations. For example, A* algorithm is an extension of Dijkstra's algorithm that uses heuristics to reduce the search space and improve efficiency.
Another popular path planning algorithm for satellite maps is the Rapidly-exploring Random Tree (RRT) algorithm. RRT is a probabilistic algorithm that generates a tree of random samples and connects them to form a path. RRT has been shown to be effective in dealing with complex environments and non-holonomic constraints. However, RRT has limitations when dealing with dynamic obstacles or multi-objective optimization problems.
In recent years, machine learning techniques have been applied to path planning for satellite maps. For example, Deep Reinforcement Learning (DRL) algorithms have been used to learn optimal paths in complex and dynamic environments. DRL algorithms use a combination of deep neural networks and reinforcement learning to learn policies that maximize a reward function. These algorithms have shown promising results in various applications, including autonomous navigation and robotics.
Another recent development in path planning for satellite maps is the integration of satellite data with other data sources such as social media and sensor data. This integration allows for more accurate and real-time path planning, which is essential in disaster response and emergency situations.
In conclusion, path planning for satellite maps is a challenging and evolving field, and several algorithms and techniques have been proposed over the years. While graph-based algorithms and RRT remain popular choices, machine learning techniques such as DRL have shown promising results. The integration of satellite data with other data sources is also a promising direction for future research.
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